watervision op• Data kinds: image2d → table
• Call: import fullseye as fs; fs.ledger.fill_level_from_image(image, pixel_pitch_um, method='edge') -> 'dict' (to call the implementation directly, import watervision; watervision.fill_level_from_image(image, pixel_pitch_um, method='edge') -> 'dict'; from the registry, opsoptics.get("fill_level_from_image"))
• GPU: none (CPU reference implementation only)
The fill line measured from a side-view bottle image -> dict (level in mm above the bottom row, row, confidence).
The row means of the image make a profile down the bottle. method="edge": the fill line is the row boundary with the strongest step of that profile (the largest absolute difference of neighbouring rows). method="brightness": the profile is split in two constant levels at the boundary that leaves the least squared error (a piecewise-constant fit), which does not depend on one sharp difference. Rows are counted from the top; the bottom row of the image is taken as the base of the bottle (as bottle_image renders it), so level_mm = (rows below the boundary) * pitch. The first liquid row is row and the step is the difference of the two levels (negative when the liquid is darker). confidence is |step| / (|step| + 2 sigma) with sigma the spread of the profile about its two levels (about 1 for a clean image, small when the profile is noise). An image that is constant, or whose row profile has no step above 1e-9 of its mean, is refused:
there is no fill line to measure (an empty or a full bottle has none). Non-finite images are refused.
Every optics op validates its input before computing (nothing slips through silently):
• Units are baked into the argument name — _mm / _um / _deg / _mrad. Confusing mm with µm does not crash; it yields a plausible-looking wrong answer, so the name prevents it. Nothing here guesses the unit from the magnitude.
• Strings raise ValueError — float('50') succeeds, so an unparsed configuration value would slip through as a length (measured: thin_lens('50', '200') returned a plausible 66.667 mm). bool is refused too, as the implicit promotion True == 1.
• complex / masked arrays raise ValueError (real-valued slots only; silently dropping the imaginary part or peeling off the mask is refused). NaN/Inf raises ValueError on every input.
• Division by zero and its relatives are refused by name: focal length 0, radius of curvature 0, refractive index <= 0, a fully opaque aperture (all zeros, so the normalisation is 0/0), a PSF whose sum is <= 0, a Stokes vector with S0 = 0, and an object sitting at the front focal point (the image is at infinity).
• Only two ops return a non-finite value, and both state it as a contract: depth_of_field returns far_mm = inf beyond the hyperfocal distance (that is what the hyperfocal distance means), and gaussian_beam returns wavefront_radius_mm = inf at the waist (the radius of curvature of a plane wavefront). Both also return a finite companion (far_is_infinite / curvature_per_mm). Any other silent NaN/Inf is detected internally and raises ValueError — "float64 overflowed" and "the answer is infinite" are different claims, so the first is never returned wearing the face of the second.
• Size caps: generated grids are capped by optics.MAX_GRID (4096); supplied fields/PSFs/apertures by optics.MAX_FIELD_ELEMENTS (2^24); ABCD element chains by optics.MAX_SYSTEM_ELEMENTS (1024); Zernike by MAX_ZERNIKE_TERMS (512) / MAX_ZERNIKE_ORDER (40) / MAX_ZERNIKE_BASIS (2^25). This closes, fail-closed, the paths where a small argument triggers a huge internal allocation (measured: n_max=40 × 4096² needs 108 GB).
• Physically impossible states are refused too: a Stokes vector with degree of polarisation > 1, negative transmittance, negative intensity, and invalid Zernike indices such as n-|m| odd.
• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).
• Operator provenance and references — the sources of the research/methods this op family came from.
• The canonical algorithm (author, year) and its uses are named in the family usage guide above.
• poc_water_inspection_images — py -3.11 examples/poc_water_inspection_images.py
table as input)abcd_matrix · wavefront_stats · sfr_from_edge · paraxial_trace · seidel_coefficients · spot_stats · tolerance_analysis · wavefront_from_opd
watervision)caustic_irradiance · bottle_image · wet_surface_appearance · wet_dry_inspection_pair · underwater_inspection_image · caustic_floor_segmentation
*Provenance: watervision.py — OPTICS operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.